Abstract #0736
Consensus between pipelines in whole-brain structural connectivity networks
Christopher S Parker 1,2 , Fani Deligianni 2 , M. Jorge Cardoso 1 , Pankaj Daga 1 , Marc Modat 1 , Chris A Clark 2 , Sebastien Ourselin 1,3 , and Jonathan D Clayden 2
1
Centre for Medical Image Computing, UCL,
London, United Kingdom,
2
Imaging
and Biophysics Unit, UCL, London, United Kingdom,
3
Dementia
Research Centre, UCL, London, United Kingdom
A variety of image processing pipelines have been used
to reconstruct whole-brain structural connectivity
networks from diffusion MRI data. The choice of
reconstruction method can impact network topology
measures. We assessed similarity in networks obtained
using two alternative and independent state-of-the-art
reconstruction pipelines in order to identify core
connections emerging robustly in both. We found high
convergence between group-averaged networks across a
range of network densities and identified a consensus
network, which had high convergence and anatomical
plausibility. Future work will investigate convergence
using finer node scale parcellations, allowing a more
detailed analysis of the convergence structure.
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